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Statistical Learning Theory
This page contains resources about Statistical Learning Theory and Computational Learning Theory. Subfields and Concepts * Vapnik-Chervonenkis(VC) Theory ** VC dimension ** Symmetrization ** Chernoff Bounds * Kernel Methods * Support Vector Machines * Probably Approximately Correct (PAC) Learning * No-Free Lunch Theorems * Boosting * Online Learning and Online Convex Optimization ** Regret Bounds ** Online Gradient Descent ** Stochastic Gradient Descent (SGD) ** Follow The Regularized Leader (FTRL) ** Multi-Armed Bandit (MAB) ** Regularization *** Matrix Regularization * Reinforcement Learning Online Courses Video Lectures *Multi-Task Learning and Matrix Regularization by Andreas Argyriou - VideoLectures.Net Lecture Notes * CS229T/STATS231:Statistical Learning Theory by Percy Liang * CS 281B / Stat 241B: Statistical Learning Theory by Peter Bartlett and Wouter Koolen * Statistical Learning Theory by Prof. Dmitry Panchenko * Statistical Learning Theory and Applications by Tomaso Poggio and Lorenzo Rosasco * Comp 236: Computational Learning Theory by Roni Khardon * CS 7545: Machine Learning Theory by Maria Florina Balcan * Foundations of Machine Learning by Mehryar Mohri * Computational and Statistical Learning Theory by Nati Srebro * Foundations of Machine Learning by Rob Schapire Books and Book Chapters * Kearns, M. J. (1990). The Computational Complexity of Machine Learning. MIT press. * Natarajan, B. K. (1991). Machine Learning: A Theoretical Approach. Morgan Kaufmann. * Kearns, M. J., & Vazirani, U. V. (1994). An Introduction to Computational Learning Theory. MIT press. * Devroye, L., Györfi, L., & Lugosi, G. (1997). A Probabilistic Theory of Pattern Recognition (Vol. 31). Springer Science & Business Media. * Anthony, M. H. G., & Biggs, N. (1997). Computational Learning Theory. Cambridge University Press. * Mitchell, T. M. (1997). "Chapter 7: Computational Learning Theory". Machine Learning. McGraw Hill. * Vapnik, V. N., & Vapnik, V. (1998). Statistical Learning Theory (Vol. 1). New York: Wiley. * Vapnik, V. (1999). The Nature of Statistical Learning Theory. Springer Science & Business Media. * Devroye, L., & Lugosi, G. (2001). Combinatorial methods in density estimation. Springer Science & Business Media. * Vapnik, V. (2006). Estimation of dependences based on empirical data. Springer Science & Business Media. * Hastie, T., Tibshirani, R., Friedman, J., Hastie, T., Friedman, J., & Tibshirani, R. (2009). The Elements of Statistical Learning (Vol. 2, No. 1). 2nd Ed. New York: Springer. * Mohri, M., Rostamizadeh, A., & Talwalkar, A. (2012). Foundations of Machine Learning. MIT press. * Shalev-Shwartz, S., & Ben-David, S. (2014). Understanding Machine Learning: From Theory to Algorithms. Cambridge University Press. * Hazan, E. (2016). Introduction to online convex optimization. Foundations and Trends® in Optimization, 2''(3-4), 157-325. Scholarly Articles * Vapnik, V. N. (1999). An overview of statistical learning theory. ''IEEE transactions on neural networks, 10(5), 988-999. * Bousquet, O., Boucheron, S., & Lugosi, G. (2004). Introduction to Statistical Learning Theory. In Advanced Lectures on Machine Learning (pp. 169-207). Springer Berlin Heidelberg. * Shalev-Shwartz, S. (2011). Online learning and online convex optimization.Foundations and Trends in Machine Learning, 4(2), 107-194. * Villa, S., Rosasco, L. & Poggio, T. (2013). On Learning, Complexity and Stability. arXiv:1303.5976 stat.ML. Tutorials * Stochastic Optimization by N. Srebro and A. Tewari - ICML 2010 See also * Dimensionality Reduction (e.g. PCA) Software Other Resources *Statistical Learning Theory - Metacademy *Statistical Learning Theory - Notes Category:Machine Learning Category:Probability and Statistics